Development of a Raspberry Pi Drowsiness Detection System based on Histogram of Oriented Gradient (HOG) Algorithm and Eye Aspect Ratio (EAR) Formula

Driver’s drowsiness is one of the major contributing factors towards the increasing
number of accidents in the world. Although there are numerous studies to develop a
drowsiness detector system based on driver’s physiological and vehicle-based
measures, there are only a few researches conducted to develop a drowsiness detector
based on driver’s behavioral measures such as yawning, eye closure or head nodding
patterns. Also, another motivation for this research is that most of the drowsiness
detection system are only implemented to continental cars, but not for local or
inexpensive cars. This is due to the systems’ high-power usage nature, usage of
expensive technologies and difficulties in integrating the detection system into all
vehicles’ system. Therefore, a Raspberry Pi drowsiness detection system based on
Histogram of Oriented Gradient (HOG) algorithm and Eye Aspect Ratio (EAR)
formula has been developed in this research. The proposed system in this paper
constantly acquires the image of the driver’s face through the attached front camera,
conducts two phases of image analyzation, which are detection of facial structure and
localization of the eyes and further monitor the changes in the eye aspect ratio values
acquired from the image analyzation to detect whether the driver is drowsy or not.
The proposed system has also achieved low power consumption and high quality of
effectiveness and accuracy in detecting drowsiness. A total number of nine
experiments such as placement on different angles and detecting different face
positons were conducted to assess the effectiveness and accuracy of the developed
system. Hence, efficient analyzation and lower battery usage are assured in the usage
of the system. As for further enhancement, yawning monitorization can be integrated
with the system for better analyzation and detection. As overall, this paper proposes
the development of a cost and power saving and effective drowsiness detection
system by implementing EAR formula and HOG algorithm, which would be easily
fixed and utilized in all type of four-wheeled vehicles.